Introduction
An artificial intelligence-oriented technique on the basis of which the model can search the directory. The searching platform used this technique in order to get better results or to limit the search. It is a very old technique not only found in machine learning, deep learning, or artificial intelligence but also in design problems associated with engineering drawing, dynamics, and statics as well. Mean end analysis is a sort of simulation technique on which the computer, system, or model applied an analysis. The model that has been created in the environment will have all the obstacles from the initial point to the final point. In an article, the author used the term “divide and conquer” for mean-end analysis. A mean-end analysis tool has been used where the bigger target has been divided into smaller ones so that an easy process can be applied on the basis of better strategy as well.
Importance of Mean End Analysis
In artificial intelligence, machine learning, and deep learning we have come across different complex computational techniques which are very hard to solve on the very first go. So, to solve them better different developers and researchers have recommended MEA, as in MEA we have divided these giant tasks into smaller ones. The division helps to make better decisions as the model is already trained for such small tasks, also lesser computational power is required, and most importantly if the goal has been achieved in between no more computations have been required. It is also important in AI, as it limits the search, and the model can find the goal more accurately with lesser computational time. This MEA technique has also increased the resolution power so that the work can be solved better as compared to the conventional machine learning and artificial intelligence technique. The other two most important concepts associated with the MEA technique are; that they have defined the goal and provided the right action plan before the execution of the real plan.
Working of Mean End Analysis
A brief introduction earlier helps the basic concept of the mean end analysis. Now the most important part has come which is how this technique works in AI. Following are a few important points associated with the working of mean end analysis are enlisted.
- The agent of the model, firstly, finds the goal or the problem that has to be solved which is the sole purpose of the system. If the problem is identified quickly then there is half of the work has been done.
- After that goal has been defined for the system or the model. This point is very important, as on the basis of this data the model has to plan action on the basis of which one can reach the goal in order to solve the problem.
- Now as in artificial intelligence, and machine learning the goals are usually complex, large, and required large computational time. So, the system has now converted them into smaller goals, these smaller goals have been divided into further smaller goals on the basis of which computational speed is divided depending on the priority of the goal.
- When the goals have been divided into the final sub-goals, now the phase has come where the final actions have been associated with these smaller goals, as shown in the figure below.
- Now with the help of intermediate actions the system has now provided some intermediate force on the basis of which the current and next problems should have been solved. The operators that have been chosen in these steps must have been only associated with each of the sub-goals.
- In parallel, an operator has been working which keeps an eye on each of the executable steps, so that there will be no, or lesser error occurring and if there is any significant amount of error occurring the system can rectify them as well.
A Brief Visualization of MEA Techniques
Understanding terms of Psychology
An article has shown an example that is associated with psychology but proves the concept of mean end analysis in artificial intelligence as well. The author provides us with an example, if someone wants to travel between two cities, he must have chosen the better path in terms of better roads, weather, and shorter ones. Now each of these better road factors, weather, and the shorter path has now become smaller goals for the person. Now each of them has different importance like better roads are of more importance from drivers’ point of view while traveling to another city, the weather is a mediocre priority factor, and finally, the length of the path has the least priority. So, while solving the problem the driver will always consider the condition of the road even if the shorter path is sacrificed. In this way, the mean end analysis has worked and is usually applied in google maps which gives us better routes by considering different small goals.
Advantages of Mean End Analysis
There are several advantages associated with this technique a few of them are enlisted as follows.
- It can help in both forward research and backward research. In this way, goal finding can become easy.
- By dividing the larger goals into smaller parts, the technique helps to solve large problems on the first priority, and on the way back during the backward research these small problems can be solved easily as the model has now trained enough to solve each of them.
- Lesser computational time is required for such a technique as the workload is divided by dividing the goals.
Conclusion
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